Summary of Longagent: Scaling Language Models to 128k Context Through Multi-agent Collaboration, by Jun Zhao et al.
LongAgent: Scaling Language Models to 128k Context through Multi-Agent Collaboration
by Jun Zhao, Can Zu, Hao Xu, Yi Lu, Wei He, Yiwen Ding, Tao Gui, Qi Zhang, Xuanjing Huang
First submitted to arxiv on: 18 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces LongAgent, a novel method that enables large language models (LLMs) like LLaMA to process long texts of up to 128K tokens. Currently, even advanced models like GPT-4 struggle with such inputs, leading to “lost in the middle” errors. LongAgent employs multi-agent collaboration, where a leader directs team members to acquire information from documents, resolving conflicts through inter-member communication mechanisms. The experimental results demonstrate significant improvements in tasks like 128k-long text retrieval and multi-hop question answering when using LLaMA-7B-based agents compared to GPT-4. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine you want a computer to understand really long texts, but it keeps getting stuck halfway through. This is a problem that even the most advanced computers have trouble with. Scientists developed a new way called LongAgent that helps these computers work better with long texts. It’s like a team working together, where one person (the leader) gives instructions and others do research. They then share information to make sure they’re all on the same page. This new method helped the computer process much longer texts than before, which could be useful for things like searching through huge amounts of text or answering really tricky questions. |
Keywords
» Artificial intelligence » Gpt » Llama » Question answering
